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research-article

Generating concept based API element comparison using a knowledge graph

Authors:

Christoph Treude,

Zhenchang Xing,

Xiaoxin ZhangAuthors Info & Claims

ASE '20: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

Pages 834 - 845

https://doi.org/10.1145/3324884.3416628

Published: 27 January 2021 Publication History

Abstract

Developers are concerned with the comparison of similar APIs in terms of their commonalities and (often subtle) differences. Our empirical study of Stack Overflow questions and API documentation confirms that API comparison questions are common and can often be answered by knowledge contained in API reference documentation. Our study also identifies eight types of API statements that are useful for API comparison. Based on these findings, we propose a knowledge graph based approach APIComp that automatically extracts API knowledge from API reference documentation to support the comparison of a pair of API classes or methods from different aspects. Our approach includes an offline phase for constructing an API knowledge graph, and an online phase for generating an API comparison result for a given pair of API elements. Our evaluation shows that the quality of different kinds of extracted knowledge in the API knowledge graph is generally high. Furthermore, the comparison results generated by APIComp are significantly better than those generated by a baseline approach based on heuristic rules and text similarity, and our generated API comparison results are useful for helping developers in API selection tasks.

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Cited By

Huang QSun YXing ZCao YChen JXu XJin HLu J(2024)Let’s Discover More API Relations: A Large Language Model-Based AI Chain for Unsupervised API Relation InferenceACM Transactions on Software Engineering and Methodology10.1145/368046933:8(1-34)Online publication date: 23-Jul-2024
https://dl.acm.org/doi/10.1145/3680469
Huang QLuo ZXing ZZeng JChen JXu XChen Y(2024)Revealing the Unseen: AI Chain on LLMs for Predicting Implicit Dataflows to Generate Dataflow Graphs in Dynamically Typed CodeACM Transactions on Software Engineering and Methodology10.1145/367245833:7(1-29)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3672458
Yu SFang CDu MDing ZChen ZSu Z(2024)Practical, Automated Scenario-Based Mobile App TestingIEEE Transactions on Software Engineering10.1109/TSE.2024.341467250:7(1949-1966)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3414672
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Index Terms

Generating concept based API element comparison using a knowledge graph
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction
2. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Documentation

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cover image ACM Conferences

ASE '20: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

December 2020

1449 pages

ISBN:9781450367684

DOI:10.1145/3324884

General Chair:
John Grundy,
Program Chairs:
Claire Le Goues,
David Lo

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 27 January 2021

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National Natural Science Foundation of China

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ASE '20

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ASE '20: 35th IEEE/ACM International Conference on Automated Software Engineering

December 21 - 25, 2020

Virtual Event, Australia

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Cited By

Huang QSun YXing ZCao YChen JXu XJin HLu J(2024)Let’s Discover More API Relations: A Large Language Model-Based AI Chain for Unsupervised API Relation InferenceACM Transactions on Software Engineering and Methodology10.1145/368046933:8(1-34)Online publication date: 23-Jul-2024
https://dl.acm.org/doi/10.1145/3680469
Huang QLuo ZXing ZZeng JChen JXu XChen Y(2024)Revealing the Unseen: AI Chain on LLMs for Predicting Implicit Dataflows to Generate Dataflow Graphs in Dynamically Typed CodeACM Transactions on Software Engineering and Methodology10.1145/367245833:7(1-29)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3672458
Yu SFang CDu MDing ZChen ZSu Z(2024)Practical, Automated Scenario-Based Mobile App TestingIEEE Transactions on Software Engineering10.1109/TSE.2024.341467250:7(1949-1966)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3414672
Li BQuan HWang JLiu PCai HMiao YYang YLi L(2024)Neural Library Recommendation by Embedding Project-Library Knowledge GraphIEEE Transactions on Software Engineering10.1109/TSE.2024.339350450:6(1620-1638)Online publication date: 24-Apr-2024
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